Harvey | Former Upstart, Vision Fund, Sequoia, Google | F.O.E. 🇨🇦
London, England
Joined February 2009
- Tweets2.1K
- Following1K
- Followers2.4K
- Likes1.4K
Andrew Kovacs retweeted
We’re not aware of any law firm going to this extreme.
For example Latham is buying GPUs but is also firm-wide on Harvey and we announced a deeper partnership today.
We think that’s the right strategy and what matters more than build vs buy is how heavily a firm is investing in AI.
The best way to invest is a portfolio approach of working closely with vertical vendors and the labs and cloud providers while also self-building with open source and the frontier ecosystem.
Everything is moving too fast and law firms are too complex for a single binary approach like this post suggests.
Law firms will not rely on a single vendor nor will they build the entire stack themselves.
My view is that longer term the labs and clouds will build general intelligence infrastructure and vertical players will help customize it for each industry.
Then individual firms will leverage their unique data, human expertise and workflows to build differentiated AI to better compete against their peers and serve their clients.
Our goal is to build the trusted vertical layer and open it up through APIs so firms can build everything from custom models and context layers to agents and products with us.
And most importantly do it in a way they can trust that their data and their clients’ data is safe and only used for their benefit.
Andrew Kovacs retweeted
full stack ai
@JensenHuang at GTC:
"Here's Trajectory and Harvey using Nemotron, post-trained it and created their own frontier-level capability."
"With customization and with partners in the ecosystem that we work with, you could turn these models into the frontier.
And a result, turn your proprietary data into sovereign intelligence"
It's great to be doing work with such amazing companies like @harvey and @nvidia over here at @trajectorylabs!
Andrew Kovacs retweeted
Introducing Intake, where every ask to legal gets routed and resolved.
Requests come in through the tools your business uses, including email, Slack, Jira, and ServiceNow.
Intake gathers the right context, then routes and resolves requests using your legal team’s rules: where each request goes, what agents can resolve autonomously, and what needs a lawyer’s review.
Answers and follow-ups go back through the same channels.
Go from request to resolution faster with Intake.
Andrew Kovacs retweeted
I’m excited to announce that @harvey has acquired @guardrails_ai !
Back when we started Guardrails, our mission was to build infra that made the AI transformation of work more reliable and secure. Our open source framework helped establish the guardrails infrastructure category, and Snowglobe, our simulation software, helped our customers save millions of dollars in operating costs.
Today, legal AI is at the frontier of the knowledge work transformation, and I’m excited to carry our mission forward by bringing everything we’ve built to Harvey.
I want to thank our amazing customers, the Guardrails open source community, our team, investors and my partner-in-crime @zaydsimjee for their support throughout our journey. I’m super proud of what we built together, and can’t wait to work on the frontier of legal AI with @winstonweinberg, @gabepereyra and the incredible team they built!
harvey.ai/blog/guardrails-ai…
Andrew Kovacs retweeted
We’ve raised $550M at a $15.5B valuation, led by @DiffusionVC and @LightSpeedVP.
We’ve crossed $400M in ARR and serve 3,000 customers, including 80% of the top 100 law firms, 20% of the Fortune 500, and half of the Fortune 10.
We are investing the capital from this round into our two most important resources: people and compute.
Our product is expanding into new verticals and more specialized solutions for areas like contracting, litigation, deals, and compliance.
As usage grows, we’re investing heavily in inference and model training to deliver frontier legal intelligence at the best possible price.
Building on Tenet, our first model post-trained on open-weight models, this round will let us scale the compute and data behind future generations of our models.
We believe the most successful application-layer companies will become full-stack AI companies, building across applications, agents, and models.
Becoming a full-stack AI company will help our customers own more of their intelligence and build their vision of a frontier legal organization.
We’re also grateful to @Sequoia, @KleinerPerkins , @A16Z , @CoatueMgmt, @Conviction, @EladGil, Evantic, GIC, @GoldmanSachs, Sapphire Ventures and Whale Rock for participating in the round and backing that vision.
We’ve raised $550M at a $15.5B valuation co-led by @lightspeedvp and @DiffusionVC.
We're using this funding to help law firms, in-house legal teams, and professional services build and own their intelligence.
Andrew Kovacs retweeted
AI power users are showing up outside tech. The fastest-growing Codex adopters since February:
Legal: 108x
Sales: 41x
Recruiting: 41x
Marketing: 26x
Healthcare: 24x
Charts of the Week: a16z.news/p/charts-of-the-we…
Andrew Kovacs retweeted
Harvey is leading the wave of application-layer AI companies to own their intelligence with the Tenet series of models.
They're starting with a foundation model for law, then expanding to custom models for each customer.
This is the leading indicator for every Enterprise.
They will post-train an open-source model for their domain (accounting, sales, design, etc), then customize those models for every major customer.
@mercor is proud to partner with @harvey to make this possible.
Introducing Tenet, our first model post-trained for legal.
Tenet is a Kimi K3 base that we post-trained with @FireworksAI_HQ on a corpus of publicly available legal data, synthetic data, and human expert data simulating long-horizon legal work.
Training increases Tenet's all-pass rate by 82% on LAB and 22% on LAB Contracts relative to the Kimi K3 base model. It achieves state-of-the-art performance on LAB Contracts and places second on LAB.
These gains generalize to other leading agentic benchmarks including @mercor's Apex Agents - Corporate Law, @crosbylegal's Redline Bench, and @scale_AI's Professional Reasoning Bench.
Tenet is also optimized for token efficiency, operating at less than a fourth the cost of leading foundation models.
We additionally post-trained three specialist models for Tenet to use as subagents:
1) M&A Diligence: post-trained with @baseten on our LAB Diligence environment in an RLM harness, this model is optimized for high-scale, long-horizon tasks.
2) Review Tables: trained with @appliedcompute on our Review Table environment, this model is state-of-the-art and cost-effective at high-volume document review and structured data extraction.
3) Firm Knowledge: trained with @EngramLab on our synthetic law firm environment, this model is optimized to learn and search over a firm's knowledge via memory and structured notes.
More details on model training, environment design, benchmarking, results, and more in the article by @gabepereyra below.
What's next for Harvey’s research?
- Scaling LAB to more jurisdictions, practice areas and workflows
- Scaling compute to bring new generalist models and capabilities to Harvey
More to come soon.
Andrew Kovacs retweeted
This is a really exciting milestone for Harvey.
As AI products scale, the model layer becomes an increasingly important place to differentiate. Custom post-training gives teams the ability to shape capability around their own workloads, while driving meaningful improvements in cost.
For Harvey Review Tables, which represents 20%+ of total inference spend, Applied Compute cut inference costs by more than half.
A great example of product, data, and model development starting to reinforce each other.
Really impressive work from @nikogrupen, @gabepereyra, @ItsJulioPereyra and the team!
Andrew Kovacs retweeted
Had so much fun giving this talk at @sequoia about @harvey’s moneyball approach to building a research lab.
The biggest mistake I made in the early days of Harvey was trying to play the Yankees baseball style of frontier intelligence.
I found out the hard way that we were the Oakland As - we couldn’t raise the capital or attract the talent to build a frontier lab.
However a lot has changed since then and it now feels possible to build frontier intelligence without a frontier budget.
Winston and I’s most quoted line from Moneyball is “We can recreate him in the aggregate” when Billy Bean talks about his strategy for building the team
The talk outlines our playbook to building frontier intelligence in the aggregate and how we leveraged the frontier ecosystem to do so.
This is only now possible with inference providers like @FireworksAI_HQ and @baseten, neolabs like @trajectorylabs, @appliedcompute, and @EngramLab, data providers like @mercor, eval infra like @LangChain and many more.
I talk about how we build training data and benchmarks, work with the neolabs and training infra providers to post-train, and give an overview of our serving and eval infra to ensure post trained models work in our product.
At the end of Moneyball Billy says that if they don’t win everyone will dismiss this strategy but “if we win, with this budget, and this team, we will have changed the game”.
Every application layer company, software company, frontier ecosystem company and startup now has a massive opportunity to play moneyball for frontier intelligence.
Go change the game.
Want world class research capabilities, but don’t have the resources of a big lab?
At our recent Sovereign AI event, @gabepereyra shared @harvey ’s “moneyball” approach.
Here’s the playbook:
00:00 Introduction
00:37 Building a research lab on a budget
02:28 Legal Agent Bench, contracting, and the diligence dataset
03:57 Domain experts guiding synthetic data generation
05:23 Why Harvey open sourced its datasets
06:55 Working with the neo labs – and why more than one
08:20 Post-training in-house: building "Associate 1"
09:44 The model serving matrix: 60 countries, fallbacks, SLAs
11:05 Deciding what stays in production
12:29 Simple open source switches and model routing
13:55 Moneyball: "If we win on this budget, we change the game"
14:53 Q&A: Training with sensitive data
17:16 Q&A: Competing for research talent
18:46 Q&A: Designing rubrics that actually challenge frontier models
20:19 Q&A: Where the pipeline breaks — data, research, or infra
22:59 Q&A: The tension in open sourcing a benchmark
25:02 Q&A: Biggest remaining open problems
27:10 Q&A: Competing with horizontal products
Andrew Kovacs retweeted
Want world class research capabilities, but don’t have the resources of a big lab?
At our recent Sovereign AI event, @gabepereyra shared @harvey ’s “moneyball” approach.
Here’s the playbook:
00:00 Introduction
00:37 Building a research lab on a budget
02:28 Legal Agent Bench, contracting, and the diligence dataset
03:57 Domain experts guiding synthetic data generation
05:23 Why Harvey open sourced its datasets
06:55 Working with the neo labs – and why more than one
08:20 Post-training in-house: building "Associate 1"
09:44 The model serving matrix: 60 countries, fallbacks, SLAs
11:05 Deciding what stays in production
12:29 Simple open source switches and model routing
13:55 Moneyball: "If we win on this budget, we change the game"
14:53 Q&A: Training with sensitive data
17:16 Q&A: Competing for research talent
18:46 Q&A: Designing rubrics that actually challenge frontier models
20:19 Q&A: Where the pipeline breaks — data, research, or infra
22:59 Q&A: The tension in open sourcing a benchmark
25:02 Q&A: Biggest remaining open problems
27:10 Q&A: Competing with horizontal products
Andrew Kovacs retweeted
Proud of our team for making @harvey the first legal AI company to earn AIUC-1 certification.
Andrew Kovacs retweeted
Today, @profound is launching Aim, the first background agent purpose-built for marketers.
For months, we've been obsessed with one problem: dashboards tell you what's happening, but not how to act on the data.
Aim is the agent harness designed specifically for marketing. Aim is trained from scratch on Profound's proprietary data, grounded in our research, and understands how marketers get work done.
Aim analyzes your AI Search data, Prompt Volumes, competitive insights, and Knowledge Base to surface the opportunities worth acting on. It finds the anomalies that matter so you can spend your time on what moves the needle.
Every Project comes with a data-backed brief and recommended tasks. From there, you can:
• Chat with Aim to refine the plan
• Deploy the custom Agent in one click
• Track progress automatically
Aim is your newest teammate keeping you moving in the right direction, working 24/7.
Andrew Kovacs retweeted
finetuning open source models has been hard to justify. why invest in tuning one when a better model ships within a month? our team just changed that.
a finetune can now transfer from one model to the next at a fraction of the cost, radically improving the economics.
Andrew Kovacs retweeted
Q2 recap for @harvey
- +$100M NNARR
- 53% DAU/MAU
Key hires (including Q1)
- Anique (CPO) - prev VP of Product at Rippling
- Rachel (CMO) - prev CMO at Notion
- Brooks (CISO) - prev CISO at Roblox
- Keith (CSO) - prev CPO at Google
Product
- Agent unification - cloud agents can use all Harvey product surfaces
- Command center (EA) - monitor adoption and ROI by use case
- Contract intelligence (EA) - agentic contracting platform for enterprises
Eng
- Migration to cloud agent infrastructure
- Integrating open source inference providers
- Scaling document processing (54TB / week)
AI
- Legal Agent Bench
- Open source post training
- Published multiple research directions with partners
We invested heavily in cloud agent infrastructure at the end of last year and in Q1. In Q2 we also unified many of our product surfaces (collapsed as @winstonweinberg says) by making them all tools accessible by our cloud agents.
Prior to this, there were a lot of capabilities in Harvey that were often only discovered by power users. As cloud agents get better and our product becomes more connected we are seeing users discover more of the product by learning from their agents (see plot of product surfaces per user).
Andrew Kovacs retweeted
Z. ai's GLM 5.2 is forcing a new conversation in AI
The new benchmark is intelligence per dollar: near-frontier smart yet affordable to run a million times
Full livestream with @levie @gabepereyra and @Srasgon in link below
Andrew Kovacs retweeted
@CharlieWills208 ERUPTS FOR 40 POINTS leading Eldorado HS (NM) to a big win over Grant HS.
High Academic Guard GETS BUCKETS! Recruitment will continue to pick up this summer!
Andrew Kovacs retweeted
Harvey is doing $300M in ARR and burning 13 trillion tokens a month. They’re a completely different industry than ours, but almost everything they said maps directly onto what engineering teams are about to face. Three things stood out:
1. Harvey’s CEO, @winstonweinberg said law firms solved the ROI problem decades ago. Every associate accounts for every six minutes of work. That’s how you justify a $10 million legal bill. He said the entire world is about to hit the same problem with tokens. You just spent a billion dollars. Where’s your ROI? Nobody has an answer yet.
2. Their Head of Applied AI Research coined a term called “Quality-maxing”. For the past two years, the default was to throw every task at the most expensive frontier model and optimize purely for output quality regardless of cost. That era is done. Now the question is what quality am I getting for every dollar I spend. That’s a fundamentally different question and most teams are not set up to answer it.
3. Gabe Pereyra, their co-founder and president, made the point that model providers have misaligned incentives. They sell consumption. They are incentivized for your agents to burn as many tokens as possible. He compared it to how law firms bill by the hour. There’s inherent pricing misalignment, but competition between firms keeps it in check. The same thing needs to happen with tokens. You need options and routing.
Harvey is doing $300M in ARR. A simple query on their platform costs $20. A full contract review costs $20,000. They are one of the largest consumers of embeddings at some of the major labs. And even they are saying this is about to get way more expensive than people realize.
The tokenmaxxing era was never sustainable. Good to see the correction finally catching up.
Nobody is "qualitymaxxing" anymore.
@harvey Head of Applied Research @nikogrupen:
"That's been the story of the last two years—I'm going to achieve the highest quality possible at whatever cost, whatever latency, whatever burden on my users."
"People are thinking about it more now in terms of: what is the quality I get for some amount of money spent or time spent?"